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iTReX: Interactive exploration of mono- and combination therapy dose response profiling data

Pharmacological Research 2022年 175卷 105996-105996页

作者： ElHarouni, Dina Berker, Yannick Peterziel, Heike Gopisetty, Apurva Turunen, Laura Kreth, Sina Stainczyk, Sabine A. Oehme, Ina Pietiäinen, Vilja Jäger, Natalie Witt, Olaf Schlesner, Matthias Oppermann, Sina Bioinformatics and Omics Data Analytics German Cancer Research Center (DKFZ) Heidelberg Germany Hopp Children's Cancer Center (KiTZ) Heidelberg Germany Division of Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Clinical Cooperation Unit Pediatric Oncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Division of Neuroblastoma Genomics German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Department of Pediatric Oncology Hematology Immunology and Pulmonology Heidelberg University Hospital Heidelberg Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany

High throughput screening methods, measuring the sensitivity and resistance of tumor cells to drug treatments have been rapidly evolving. Not only do these screens allow correlating response profiles to tumor genomic features for developing novel predictors of treatment response, but they can also add evidence for therapy decision making in precision oncology. Recent analysis methods developed for either assessing single agents or combination drug efficacies enable quantification of dose-response curves with restricted symmetric fit settings. Here, we introduce iTReX, a user-friendly and interactive Shiny/R application, for both the analysis of mono- and combination therapy responses. The application features an extended version of the drug sensitivity score (DSS) based on the integral of an advanced five-parameter dose-response curve model and a differential DSS for combination therapy profiling. Additionally, iTReX includes modules that visualize drug target interaction networks and support the detection of matches between top therapy hits and the sample omics features to enable the identification of druggable targets and biomarkers. iTReX enables the analysis of various quantitative drug or therapy response readouts (e.g. luminescence, fluorescence microscopy) and multiple treatment strategies (drug treatments, radiation). Using iTReX we validate a cost-effective drug combination screening approach and reveal the application's ability to identify potential sample-specific biomarkers based on drug target interaction networks. The iTReX web application is accessible at https://*** © 2021 The Authors

关键词： Asymmetric sensitivity scoring Differential combination sensitivity scoring Drug target networks Interactive drug screen analysis Personalized medicine Therapy response profiling and exploration

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Privacy-Preserving Multi-center Differential Protein Abundance Analysis with FedProt

arXiv

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arXiv 2024年

作者： Burankova, Yuliya Abele, Miriam Bakhtiari, Mohammad von Törne, Christine Barth, Teresa Schweizer, Lisa Giesbertz, Pieter Schmidt, Johannes R. Kalkhof, Stefan Müller-Deile, Janina van Veelen, Peter A. Mohammed, Yassene Hammer, Elke Arend, Lis Adamowicz, Klaudia Laske, Tanja Hartebrodt, Anne Frisch, Tobias Meng, Chen Matschinske, Julian Späth, Julian Röttger, Richard Schwämmle, Veit Hauck, Stefanie M. Lichtenthaler, Stefan Imhof, Axel Mann, Matthias Ludwig, Christina Kuster, Bernhard Baumbach, Jan Zolotareva, Olga Chair of Proteomics and Bioanalytics TUM School of Life Sciences Technical University of Munich Freising Germany Institute for Computational Systems Biology University of Hamburg Hamburg Germany TUM School of Life Sciences Technical University of Munich Freising Germany Metabolomics and Proteomics Core Helmholtz Center Munich Munich Germany Protein Analysis Unit Biomedical Center Faculty of Medicine LMU Munich Martinsried Germany Max Planck Institute of Biochemistry Martinsried Germany Munich Germany Department of Preclinical Development and Validation Fraunhofer Institute for Cell Therapy and Immunology IZI Leipzig Germany Institute for Bioanalysis University of Applied Science Coburg Coburg Germany Department of Nephrology University Hospital Erlangen Erlangen Germany Center for Proteomics and Metabolomics Leiden University Medical Center Leiden Netherlands University of Greifswald Greifswald Germany Germany Data Science in Systems Biology TUM School of Life Sciences Technical University of Munich Freising Germany University of Southern Denmark Denmark Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Neuroproteomics School of Medicine and Health Klinikum rechts der Isar Technical University of Munich Munich Germany Munich Germany

Quantitative mass spectrometry has revolutionized proteomics by enabling simultaneous quantification of thousands of proteins. Pooling patient-derived data from multiple institutions enhances statistical power but raises significant privacy concerns. Here we introduce FedProt, the first privacy-preserving tool for collaborative differential protein abundance analysis of distributed data, which utilizes federated learning and additive secret sharing. In the absence of a multicenter patient-derived dataset for evaluation, we created two, one at five centers from LFQ *** experiments and one at three centers from TMT human serum. Evaluations using these datasets confirm that FedProt achieves accuracy equivalent to DEqMS applied to pooled data, with completely negligible absolute differences no greater than 4*10-12. In contrast, -log10(p-values) computed by the most accurate meta-analysis methods diverged from the centralized analysis results by up to 25-27. FedProt is available as a web tool with detailed documentation as a FeatureCloud App. © 2024, CC BY.

关键词： Proteins

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Detecting potentially harmful and protective suicide-related content on twitter: A machine learning approach

arXiv

引用

arXiv 2021年

作者： Metzler, Hannah Baginski, Hubert Niederkrotenthaler, Thomas Garcia, David Section for Science of Complex Systems Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Austria Unit Suicide Research & Mental Health Promotion Department of Social and Preventive Medicine Center for Public Health Medical University of Vienna Austria Complexity Science Hub Vienna Austria Institute of Interactive Systems and Data Science Department of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Institute of Globally Distributed Open Research and Education Austria Institute of Information Systems Engineering Vienna University of Technology Vienna Austria

Background Research has repeatedly shown that exposure to suicide-related news media content is associated with suicide rates, with some content characteristics likely having harmful and others potentially protective effects. Although good evidence exists for a few selected characteristics, systematic and large-scale investigations are missing. Moreover, the growing importance of social media, particularly among young adults, calls for studies on the effects of content posted on these platforms. Objective This study applies natural language processing and machine learning methods to classify large quantities of social media data according to characteristics identified as potentially harmful or beneficial in media effects research on suicide and prevention. Methods We manually labeled 3202 English tweets using a novel annotation scheme that classifies suicide-related tweets into 12 categories. Based on these categories, we trained a benchmark of machine learning models for a multi-class and a binary classification task. As models, we included a majority classifier, an approach based on word frequency (TF-IDF with a linear SVM) and two state-of-the-art deep learning models (BERT, XLNet). The first task classified postings into six main content categories, which are particularly relevant for suicide prevention based on previous evidence. These included personal stories of either (1) suicidal ideation and attempts or (2) coping and recovery, calls for action intending to spread either (3) problem awareness or (4) prevention-related information, (5) reporting of suicide cases, and (6) other tweets irrelevant to these five categories. The second classification task was binary, and separated postings in the 11 categories that refer to actual suicide, from postings in the off-topic category, which use suicide-related terms in another meaning or context. Results In both tasks, the performance of the two deep learning models was very similar and better than the majority or th

关键词： Learning algorithms

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LimeSoDa: A dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping

arXiv

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arXiv 2025年

作者： Schmidinger, Jonas Vogel, Sebastian Barkov, Viacheslav Pham, Anh-Duy Gebbers, Robin Tavakoli, Hamed Correa, Jose Tavares, Tiago R. Filippi, Patrick Jones, Edward J. Lukas, Vojtech Boenecke, Eric Ruehlmann, Joerg Schroeter, Ingmar Kramer, Eckart Paetzold, Stefan Kodaira, Masakazu Wadoux, Alexandre M.J.-C. Bragazza, Luca Metzger, Konrad Huang, Jingyi Valente, Domingos S.M. Safanelli, Jose L. Bottega, Eduardo L. Dalmolin, Ricardo S.D. Farkas, Csilla Steiger, Alexander Horst, Taciara Z. Ramirez-Lopez, Leonardo Scholten, Thomas Stumpf, Felix Rosso, Pablo Costa, Marcelo M. Zandonadi, Rodrigo S. Wetterlind, Johanna Atzmueller, Martin Osnabrück University Joint Lab Artificial Intelligence and Data Science Osnabrück Germany Department of Agromechatronics Potsdam Germany Piracicaba Brazil The University of Sydney Sydney Institute of Agriculture Sydney Australia Mendel University in Brno Department of Agrosystems and Bioclimatology Brno Czech Republic Leibniz Institute of Vegetable and Ornamental Crops Next Generation Horticultural Systems Grossbeeren Germany Eberswalde University for Sustainable Development Landscape Management and Nature Conservation Eberswalde Germany Soil Science and Soil Ecology Bonn Germany Tokyo University of Agriculture and Technology Institute of Agriculture Tokyo Japan LISAH Univ. Montpellier AgroParisTech INRAE IRD L'Institut Agro Montpellier France Agroscope Field-Crop Systems and Plant Nutrition Nyon Switzerland University of Wisconsin-Madison Department of Soil Science Madison United States Federal University of Viçosa Department of Agricultural Engineering Viçosa Brazil Woodwell Climate Research Center Falmouth United States Academic Coordination Santa Maria Brazil Soil Department Santa Maria Brazil Division of Environment and Natural Resources Aas Norway University of Rostock Chair of Geodesy and Geoinformatics Rostock Germany Federal Technological University of Paraná Dois Vizinhos Brazil BÜCHI Labortechnik AG Data Science Department Flawil Switzerland Imperial College London Imperial College Business School London United Kingdom University of Tübingen Department of Geosciences Tübingen Germany University of Tübingen DFG Cluster of Excellence Machine Learning for Science’ Germany Bern University of Applied Sciences Competence Center for Soils Zollikofen Switzerland Simulation and Data Science Müncheberg Germany Federal University of Jataí Institute of Agricultural Sciences Jatai Brazil Federal University of Mato Grosso Instute of Agricultural and Environmental Scinces Sinop Brazil Department of Soil and Environment Skara

Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number ( © 2025, CC BY-SA.

关键词： Adversarial machine learning

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Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems

arXiv

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arXiv 2021年

作者： Ozbulak, Utku Vandersmissen, Baptist Jalalvand, Azarakhsh Couckuyt, Ivo van Messem, Arnout de Neve, Wesley Department of Electronics and Information Systems Ghent University Belgium Department of Information Technology Ghent University–imec Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Department of Mechanical and Aerospace Engineering Princeton University United States Center for Biotech Data Science Ghent University Global Campus Korea Republic of

Given their substantial success in addressing a wide range of computer vision challenges, Convolutional Neural Networks (CNNs) are increasingly being used in smart home applications, with many of these applications relying on the automatic recognition of human activities. In this context, low-power radar devices have recently gained in popularity as recording sensors, given that the usage of these devices allows mitigating a number of privacy concerns, a key issue when making use of conventional video cameras. Another concern that is often cited when designing smart home applications is the resilience of these applications against cyberattacks. It is, for instance, well-known that the combination of images and CNNs is vulnerable against adversarial examples, mischievous data points that force machine learning models to generate wrong classifications during testing time. In this paper, we investigate the vulnerability of radar-based CNNs to adversarial attacks, and where these radar-based CNNs have been designed to recognize human gestures. Through experiments with four unique threat models, we show that radar-based CNNs are susceptible to both white- and black-box adversarial attacks. We also expose the existence of an extreme adversarial attack case, where it is possible to change the prediction made by the radar-based CNNs by only perturbing the padding of the inputs, without touching the frames where the action itself occurs. Moreover, we observe that gradient-based attacks exercise perturbation not randomly, but on important features of the input data. We highlight these important features by making use of Grad-CAM, a popular neural network interpretability method, hereby showing the connection between adversarial perturbation and prediction interpretability. Copyright © 2021, The Authors. All rights reserved.

关键词： Automation

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ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis (vol 53, pg 403, 2021)

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NATURE GENETICS 2021年第6期53卷 935-935页

作者： Granja, Jeffrey M. Corces, M. Ryan Pierce, Sarah E. Bagdatli, S. Tansu Choudhry, Hani Chang, Howard Y. Greenleaf, William J. Department of Genetics Stanford University School of Medicine Stanford CA USA Program in Biophysics Stanford University Stanford CA USA Center for Personal Dynamic Regulomes Stanford University Stanford CA USA Department of Pathology Stanford University School of Medicine Stanford CA USA Gladstone Institute of Neurological Disease Gladstone Institute of Data Science and Biotechnology San Francisco CA USA Department of Neurology University of California San Francisco San Francisco CA USA Program in Cancer Biology Stanford University School of Medicine Stanford CA USA Department of Biochemistry Faculty of Science Cancer and Mutagenesis Unit King Fahd Center for Medical Research King Abdulaziz University Jeddah Saudi Arabia Howard Hughes Medical Institute Stanford University Stanford CA USA Department of Applied Physics Stanford University Stanford CA USA Chan Zuckerberg Biohub San Francisco CA USA

The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://***/) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells.

关键词： Epigenetics Epigenomics Gene regulation

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The bulk motion of gas in the core of the Centaurus galaxy cluster

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Nature 2025年第8050期638卷 365-369页

作者： Majumder, Anwesh Hosogi, Kokoro Sun, Ming Plšek, Tomáš Werner, Norbert Kondo, Marie Zhuravleva, Irina Yukita, Mihoko Yoshida, Tessei Yoneyama, Tomokage Yaqoob, Tahir Yamauchi, Shigeo Yamauchi, Makoto Yamasaki, Noriko Y. Yamaoka, Kazutaka Yamaguchi, Hiroya Yamada, Shinya Yamada, Satoshi Williams, Brian J. Watanabe, Shin Vink, Jacco Uno, Shinichiro Ueda, Yoshihiro Uchiyama, Hideki Uchida, Yuusuke Uchida, Nagomi Uchida, Hiroyuki Tsuru, Takeshi G. Tsunemi, Hiroshi Tsujimoto, Masahiro Tsuboi, Yohko Trigo, María Díaz Terashima, Yuichi Terada, Yukikatsu Tashiro, Makoto Tanimoto, Atsushi Tanaka, Takaaki Tamura, Keisuke Tamagawa, Toru Takeo, Mai Takahashi, Hiromitsu Szymkowiak, Andrew Suzuki, Hiromasa Smith, Randall Simionescu, Aurora Shidatsu, Megumi Seta, Hiromi Sawada, Makoto Sato, Toshiki Sato, Kosuke Pottschmidt, Katja Porter, Frederick Scott Plucinsky, Paul Petre, Robert Paltani, Stephane Ota, Naomi Okajima, Takashi Ogorzalek, Anna Ogawa, Shoji Odaka, Hirokazu Noda, Hirofumi Nobukawa, Masayoshi Nobukawa, Kumiko Ness, Jan-Uwe Nakazawa, Kazuhiro Nakajima, Hiroshi Mushotzky, Richard Murakami, Hiroshi Mukai, Koji Mori, Koji Mizuno, Tsunefumi Mizumoto, Misaki Mitsuishi, Ikuyuki Miller, Jon M. Miller, Eric D. Mernier, François McNamara, Brian McCammon, Dan Matsushita, Kyoko Matsumoto, Hironori Markevitch, Maxim Maeda, Yoshitomo Loewenstein, Michael Leutenegger, Maurice Kubota, Aya Kohmura, Takayoshi Kobayashi, Shogo Kitamoto, Shunji Kilbourne, Caroline Kelley, Richard Kanemaru, Yoshiaki Katsuda, Satoru Kara, Erin Kallman, Timothy Kaastra, Jelle Ishisaki, Yoshitaka Ishikawa, Kumi Ishida, Manabu Ichinohe, Yuto Hornschemeier, Ann Hodges-Kluck, Edmund Hell, Natalie Hayashi, Takayuki Hayashi, Katsuhiro Hatsukade, Isamu Hamaguchi, Kenji Hagino, Kouichi Guainazzi, Matteo Gu, Liyi García, Javier A. Gallo, Luigi Furuzawa, Akihiro Fukushima, Kotaro Fukazawa, Yasushi Fujita, Yutaka Fujimoto, Ryuichi Foster, Adam Ezoe, Yuichiro Eguchi, Satoshi Enoto, Teruaki Eckert, Dominique Eckart, Megan E. Ebisawa, Ken Dotani, Tadayasu Done, Chris Department of Physics Ehime University Matsuyama Japan Department of Astronomy University of Maryland College Park MD United States NASA/Goddard Space Flight Center Greenbelt MD United States Center for Research and Exploration in Space Science and Technology NASA/GSFC (CRESST II) Greenbelt MD United States Department of Physics University of Tokyo Tokyo Japan Department of Physics Technion Haifa Israel Center for Space Science and Technology University of Maryland Baltimore County Baltimore MD United States Center for Astrophysics | Harvard-Smithsonian Cambridge MA United States Lawrence Livermore National Laboratory Livermore CA United States Department of Astronomy University of Michigan Ann Arbor MI United States SRON Netherlands Institute for Space Research Leiden Netherlands Centre for Extragalactic Astronomy Department of Physics University of Durham Durham United Kingdom Institute of Space and Astronautical Science (ISAS) Japan Aerospace Exploration Agency (JAXA) Kanagawa Japan Department of Physics Kyoto University Kyoto Japan Department of Economics Kumamoto Gakuen University Kumamoto Japan Department of Physics Tokyo Metropolitan University Tokyo Japan Department of Physics Hiroshima University Hiroshima Japan Department of Physics Fujita Health University Toyoake Japan Department of Astronomy and Physics Saint Mary’s University Halifax Nova Scotia Canada Cahill Center for Astronomy and Astrophysics California Institute of Technology Pasadena CA United States European Space Agency (ESA) European Space Research and Technology Centre (ESTEC) Noordwijk Netherlands Faculty of Engineering University of Miyazaki Miyazaki Japan RIKEN Nishina Center Saitama Japan Leiden Observatory University of Leiden Leiden Netherlands Kavli Institute for Astrophysics and Space Research Massachusetts Institute of Technology Cambridge MA United States Department of Physics Saitama University Saitama Japan Department of Physics Rikkyo Universi

Galaxy clusters contain vast amounts of hot ionized gas known as the intracluster medium (ICM). In relaxed cluster cores, the radiative cooling time of the ICM is shorter than the age of the cluster. However, the absence of line emission associated with cooling suggests heating mechanisms that offset the cooling, with feedback from active galactic nuclei (AGNs) being the most likely source^1,2. Turbulence and bulk motions, such as the oscillating (‘sloshing’) motion of the core gas in the cluster potential well, have also been proposed as mechanisms for heat distribution from the outside of the core^3,4. Here we present X-ray spectroscopic observations of the Centaurus galaxy cluster with the X-Ray Imaging and Spectroscopy Mission satellite. We find that the hot gas flows along the line of sight relative to the central galaxy, with velocities from 130 km s⁻¹ to 310 km s⁻¹ within about 30 kpc of the centre. This indicates bulk flow consistent with core gas sloshing. Although the bulk flow may prevent excessive accumulation of cooled gas at the centre, it could distribute the heat injected by the AGN and bring in thermal energy from the surrounding ICM. The velocity dispersion of the gas is found to be only ≲120 km s⁻¹ in the core, even within about 10 kpc of the AGN. This suggests that the influence of the AGN on the surrounding ICM motion is limited in the cluster. © The Author(s), under exclusive licence to Springer Nature Limited 2025.

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Topological bias: How haloes trace structural patterns in the cosmic web

arXiv

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arXiv 2022年

作者： Bermejo, Raul Wilding, Georg van de Weygaert, Rien Jones, Bernard J.T. Vegter, Gert Efstathiou, Konstantinos Kapteyn Astronomical Institute University of Groningen PO Box 800 Groningen9700 AV Netherlands Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen PO Box 800 Groningen9700 AV Netherlands Centre for Data Science and Systems Complexity University of Groningen PO Box 800 Groningen9700 AV Netherlands Division of Natural and Applied Sciences Zu Chongzhi Center for Mathematics and Computational Science Duke Kunshan University No. 8 Duke Avenue Jiangsu Province Kunshan215316 China

We trace the connectivity of the cosmic web as defined by haloes in the Planck-Millennium simulation using a persistence and Betti curve analysis. We normalise clustering up to the second-order correlation function, and use our systematic topological analysis to correlate local information and properties of haloes with their multi-scale geometrical environment of the cosmic web (elongated filamentary bridges and sheetlike walls). We capture the multi-scale topology traced by the halo distribution through filtrations of the corresponding Delaunay tessellation. The resulting nested alpha shapes are sensitive to the local density, perfectly outline the local geometry, and contain the complete information on the multi-scale topology. We find a remarkable linear relationship between halo masses and topology: haloes of different mass trace environments with different topological signature. This is topological bias, an environmental structure bias independent of the halo clustering bias associated with the two-point correlation function. This mass-dependent linear scaling relation allows us to take clustering into account and determine the overall connectivity from a limited sample of galaxies. The presence of topological bias has major implications for the study of voids and filaments in the observed distribution of galaxies. The (infra)structure and shape of these key cosmic web components will strongly depend on the underlying galaxy sample. Their use as cosmological probes, with their properties influenced by cosmological parameters, will have to account for the subtleties of topological bias. This is of particular relevance with the large upcoming galaxy surveys such as DESI, Euclid, and the Vera Rubin telescope surveys. Copyright © 2022, The Authors. All rights reserved.

关键词： Galaxies

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Lightning graph matching

arXiv

引用

arXiv 2023年

作者： Shen, Binrui Niu, Qiang Zhu, Shengxin Department of Applied Mathematics School of Mathematics and Physics Xi'an Jiaotong-Liverpool University Suzhou215123 China Department of Mathematical Sciences School of Physical Sciences University of Liverpool Liverpool United Kingdom School of Mathematical Sciences Laboratory of Mathematics and Complex Systems MOE Beijing Normal University Beijing100875 China Faculty of Arts and Sciences Beijing Normal University Zhuhai519087 China Research Center for Mathematics Advanced Institute of Natural Science Beijing Normal University Zhuhai519087 China Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai519087 China

The spectral matching algorithm is a classic method for finding correspondences between two graphs, a fundamental task in pattern recognition. It has a time complexity of O(n4) and a space complexity of O(n4), where n is the number of nodes. However, such complexity limits its applicability to large-scale graph matching tasks. This paper proposes a redesign of the algorithm by transforming the graph matching problem into a one-dimensional linear assignment problem. This transformation enables efficient solving by sorting two n × 1 vectors. The resulting algorithm is named the Lightning Spectral Assignment Method (LiSA), which enjoys a complexity of O(n2). Numerical experiments demonstrate the efficiency of LiSA, supported by theoretical analysis. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph algorithms

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Feature Extraction and Classification from Planetary science datasets enabled by Machine Learning

Feature Extraction and Classification from Planetary Science...

引用

IEEE Conference on Aerospace

作者： Conor A. Nixon Zachary Yahn Ethan Duncan Ian Neidel Alyssa C. Mills Benoît Seignovert Andrew Larsen Kathryn Gansler Charles Liles Catherine C. Walker Douglas M. Trent John Santerre Planetary Systems Laboratory NASA Goddard Space Flight Center Greenbelt MD Computer Science Department University of Virginia Charlottesville VA School of Information University of California Berkeley Berkeley CA Yale University Timothy Dwight College New Haven CT Geosciences Department Baylor University Waco TX Nantes Université Laboratoire De Planétologie Et Géosciences Nantes France Business Intelligence Division Inntopia Stowe VT Department of Geology University of Maryland College Park MD NASA Langley Research Center Center Operations Directorate Virginia Department of Applied Ocean Physics and Engineering Woods Hole Oceanographic Institution Massachusetts OCIO Information Data & Analytics Services SAIC | East-2 NASA Langley Research Center Virginia

In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition. Our first investigation was to recognize ice blocks (also known as rafts, plates, polygons) in the chaos regions of fractured ice on Europa. We used a transfer learning approach, adding and training new layers to an industry-standard Mask R-CNN (Region-based Convolutional Neural Network) to recognize labeled blocks in a training dataset. Subsequently, the updated model was tested against a new dataset, achieving 68% precision. In a different application, we applied the Mask R-CNN to recognize clouds on Titan, again through updated training followed by testing against new data, with a precision of 95% over 369 images. We evaluate the relative successes of our techniques and suggest how training and recognition could be further improved. The new approaches we have used for planetary datasets can further be applied to similar recognition tasks on other planets, including Earth. For imagery of outer planets in particular, the technique holds the possibility of greatly reducing the volume of returned data, via onboard identification of the most interesting image subsets, or by returning only differential data (images where changes have occurred) greatly enhancing the information content of the final data stream.

关键词： Training Earth Image recognition Transfer learning Streaming media Feature extraction Ice

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